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# Data Engineer & Insights Analyst - Contract - Remote

- **Company:** Infoorigin inc -
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $187,200 - $208,000
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Airflow, Amazon Web Services, Amazon S3, Business Analytics Applications, Big Data, Cloud Database, Continuous Integration, Data Architecture, Data Validation, Information Engineering, Data Governance, Data Integration, Extract Transform Load (ETL), Data Migration, Data Systems, Data Vault Modeling, Dimensional Modeling, Apache Hadoop, Python (Programming Language), PostgreSQL, Meta-Data Management, Microsoft SQL Server, NoSQL, Oracle (Applications), Cloud Services, Standard Sql, Scala (Programming Language), SQL Databases, Systems Integration, Talend, Workflow Management Systems, Data Logging, Informatica Powercenter, Apache Spark, Git, Data Lineage, Luigi, Cassandra, Star Schema, Apache Kafka, Restful APIs, Data Pipelines, TIBCO (Software), Mulesoft
- **Published:** November 7, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=3bd0e1cd14d7093e

## About the Role

+ Minimum 10+ years of experience in Data Engineering, Data Integration, or related roles.
 + Strong hands-on experience with AWS cloud services, including S3, Glue, Redshift, EMR, Lambda, and Athena.
 + Proficiency in programming languages such as Python, Java, Scala, or R.
 + Advanced knowledge of SQL and relational/NoSQL databases such as Oracle, MS SQL Server, PostgreSQL, and Cassandra.
 + Experience with Big Data technologies including Apache Spark, Hadoop, and Kafka.
 + Hands-on experience with ETL tools such as Informatica, MuleSoft, Talend, or TIBCO.
 + Knowledge of Apache Airflow, Luigi, Azkaban, or similar workflow management tools.
 + Experience with Git, CI/CD pipelines, REST APIs, and data integration.
 + Strong understanding of data modeling, dimensional modeling, Star/Snowflake schemas, and Data Vault.
 + Knowledge of data governance, metadata management, data lineage, and data quality frameworks.
 + Strong analytical, problem-solving, project management, and communication skills.
 * Preferred Experience:
 + Experience working with higher education or student-level datasets.
 + Knowledge of integrating education data with employment and wage records.
 + Experience handling large, complex, and confidential datasets.
 + Experience developing analytics solutions, dashboards, and reports for business or government organizations., A strong Data Engineer with a combination of AWS cloud expertise, Python/SQL programming, ETL development, and Big Data experience, along with the ability to transform complex datasets into meaningful insights and support data-driven decision-making.

## Description

We are looking for an experienced Data Engineer & Insights Analyst with 10+ years of experience in designing, developing, and maintaining scalable data pipelines and cloud-based data solutions. The ideal candidate should have strong expertise in AWS services, Python, SQL, ETL processes, and Big Data technologies.
The candidate will be responsible for collecting, integrating, transforming, and analyzing large and complex datasets to support business intelligence, reporting, and data-driven decision-making. The role also involves developing data architecture, improving data quality, and collaborating with data scientists, analysts, and business teams., + Design, develop, and maintain scalable data pipelines and data architecture.

 + Build cloud-based data solutions using AWS services such as S3, Glue, Redshift, EMR, Athena, Lambda, and RDS.
 + Develop and optimize ETL/ELT processes using Python, SQL, Java, or Scala.
 + Integrate, transform, and manage large datasets from multiple sources.
 + Work with Big Data technologies such as Apache Spark, Hadoop, and Kafka.
 + Build and manage data orchestration workflows using Apache Airflow, AWS Step Functions, or similar tools.
 + Develop and maintain relational and NoSQL databases.
 + Create data models, dashboards, reports, and analytical solutions to support business decision-making.
 + Implement data quality checks, monitoring, logging, and validation processes.
 + Ensure data governance, security, privacy, and compliance standards.
 + Support data migration, conversion, and system modernization projects.
 + Collaborate with data scientists, research analysts, and business stakeholders to deliver reliable data solutions.